






"""simple docstring"""


import warnings
from typing import List, Optional, Tuple, Union

import numpy as np
import PIL
import torch

from ...models import UNetaDModel
from ...schedulers import RePaintScheduler
from ...utils import PIL_INTERPOLATION, logging, randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput


__SCREAMING_SNAKE_CASE   =   logging.get_logger(__name__)  # pylint: disable=invalid-name


def        UpperCAmelCase      (					a__ ):





						'''simple docstring'''






						warnings.warn(
						    'The preprocess method is deprecated and will be removed in a future version. Please'
						    ' use VaeImageProcessor.preprocess instead'     ,      a__     ,      )
						if isinstance(a__     ,      torch.Tensor ):
												return image
						elif isinstance(a__     ,      PIL.Image.Image ):
												lowerCAmelCase   :int						=			[image]

						if isinstance(image[0]     ,      PIL.Image.Image ):
												lowerCAmelCase	, lowerCAmelCase   :Optional[int]						=			image[0].size
												lowerCAmelCase	, lowerCAmelCase   :Tuple						=			(x - x % 8 for x in (w, h))  # resize to integer multiple of 8

												lowerCAmelCase   :int						=			[np.array(i.resize((w, h)     ,      resample=PIL_INTERPOLATION['lanczos'] ) )[None, :] for i in image]
												lowerCAmelCase   :int						=			np.concatenate(a__     ,      axis=0 )
												lowerCAmelCase   :Optional[Any]						=			np.array(a__ ).astype(np.floataa ) / 255.0
												lowerCAmelCase   :str						=			image.transpose(0     ,      3     ,      1     ,      2 )
												lowerCAmelCase   :List[Any]						=			2.0 * image - 1.0
												lowerCAmelCase   :Dict						=			torch.from_numpy(a__ )
						elif isinstance(image[0]     ,      torch.Tensor ):
												lowerCAmelCase   :Union[str, Any]						=			torch.cat(a__     ,      dim=0 )
						return image


def        UpperCAmelCase      (					a__ ):





						'''simple docstring'''






						if isinstance(a__     ,      torch.Tensor ):
												return mask
						elif isinstance(a__     ,      PIL.Image.Image ):
												lowerCAmelCase   :Union[str, Any]						=			[mask]

						if isinstance(mask[0]     ,      PIL.Image.Image ):
												lowerCAmelCase	, lowerCAmelCase   :Optional[int]						=			mask[0].size
												lowerCAmelCase	, lowerCAmelCase   :Any						=			(x - x % 32 for x in (w, h))  # resize to integer multiple of 32
												lowerCAmelCase   :List[str]						=			[np.array(m.convert('L' ).resize((w, h)     ,      resample=PIL_INTERPOLATION['nearest'] ) )[None, :] for m in mask]
												lowerCAmelCase   :Union[str, Any]						=			np.concatenate(a__     ,      axis=0 )
												lowerCAmelCase   :List[Any]						=			mask.astype(np.floataa ) / 255.0
												lowerCAmelCase   :List[Any]						=			0
												lowerCAmelCase   :Optional[int]						=			1
												lowerCAmelCase   :str						=			torch.from_numpy(a__ )
						elif isinstance(mask[0]     ,      torch.Tensor ):
												lowerCAmelCase   :Optional[int]						=			torch.cat(a__     ,      dim=0 )
						return mask







class 						__UpperCamelCase     (     UpperCamelCase						):
					lowercase_    :				UNetaDModel
					lowercase_    :				RePaintScheduler
					def __init__(							self							:	int     , UpperCAmelCase							:	Union[str, Any]     , UpperCAmelCase							:	Union[str, Any]   )	->       Tuple:
											super().__init__()
											self.register_modules(unet=UpperCAmelCase     , scheduler=UpperCAmelCase   )
					@torch.no_grad()
					def __call__(							self							:	Union[str, Any]     , UpperCAmelCase							:	Union[torch.Tensor, PIL.Image.Image]     , UpperCAmelCase							:	Union[torch.Tensor, PIL.Image.Image]     , UpperCAmelCase							:	int = 250     , UpperCAmelCase							:	float = 0.0     , UpperCAmelCase							:	int = 10     , UpperCAmelCase							:	int = 10     , UpperCAmelCase							:	Optional[Union[torch.Generator, List[torch.Generator]]] = None     , UpperCAmelCase							:	Optional[str] = "pil"     , UpperCAmelCase							:	bool = True     , )	->       Union[ImagePipelineOutput, Tuple]:
											lowerCAmelCase   :List[str]						=			image

											lowerCAmelCase   :Optional[int]						=			_preprocess_image(UpperCAmelCase   )
											lowerCAmelCase   :str						=			original_image.to(device=self.device     , dtype=self.unet.dtype   )
											lowerCAmelCase   :Union[str, Any]						=			_preprocess_mask(UpperCAmelCase   )
											lowerCAmelCase   :Union[str, Any]						=			mask_image.to(device=self.device     , dtype=self.unet.dtype   )

											lowerCAmelCase   :Dict						=			original_image.shape[0]

											# sample gaussian noise to begin the loop
											if isinstance(UpperCAmelCase     , UpperCAmelCase   ) and len(UpperCAmelCase   ) != batch_size:
																	raise ValueError(
																	    f"""You have passed a list of generators of length {len(UpperCAmelCase   )}, but requested an effective batch"""
																	    f""" size of {batch_size}. Make sure the batch size matches the length of the generators."""   )

											lowerCAmelCase   :str						=			original_image.shape
											lowerCAmelCase   :List[Any]						=			randn_tensor(UpperCAmelCase     , generator=UpperCAmelCase     , device=self.device     , dtype=self.unet.dtype   )

											# set step values
											self.scheduler.set_timesteps(UpperCAmelCase     , UpperCAmelCase     , UpperCAmelCase     , self.device   )
											lowerCAmelCase   :Union[str, Any]						=			eta

											lowerCAmelCase   :Optional[Any]						=			self.scheduler.timesteps[0] + 1
											lowerCAmelCase   :List[str]						=			generator[0] if isinstance(UpperCAmelCase     , UpperCAmelCase   ) else generator
											for i, t in enumerate(self.progress_bar(self.scheduler.timesteps   )   ):
																	if t < t_last:
																							# predict the noise residual
																							lowerCAmelCase   :int						=			self.unet(UpperCAmelCase     , UpperCAmelCase   ).sample
																							# compute previous image: x_t -> x_t-1
																							lowerCAmelCase   :List[Any]						=			self.scheduler.step(UpperCAmelCase     , UpperCAmelCase     , UpperCAmelCase     , UpperCAmelCase     , UpperCAmelCase     , UpperCAmelCase   ).prev_sample

																	else:
																							# compute the reverse: x_t-1 -> x_t
																							lowerCAmelCase   :Optional[Any]						=			self.scheduler.undo_step(UpperCAmelCase     , UpperCAmelCase     , UpperCAmelCase   )
																	lowerCAmelCase   :Dict						=			t

											lowerCAmelCase   :str						=			(image / 2 + 0.5).clamp(0     , 1   )
											lowerCAmelCase   :List[str]						=			image.cpu().permute(0     , 2     , 3     , 1   ).numpy()
											if output_type == "pil":
																	lowerCAmelCase   :List[Any]						=			self.numpy_to_pil(UpperCAmelCase   )

											if not return_dict:
																	return (image,)

											return ImagePipelineOutput(images=UpperCAmelCase   )







"""simple docstring"""


import qiskit


def        UpperCAmelCase      (					a__     ,      a__ ):





						'''simple docstring'''






						lowerCAmelCase   :List[Any]						=			qiskit.Aer.get_backend('aer_simulator' )

						# Create a Quantum Circuit acting on the q register
						lowerCAmelCase   :Optional[int]						=			qiskit.QuantumCircuit(a__     ,      a__ )

						# Apply X (NOT) Gate to Qubits 0 & 1
						circuit.x(0 )
						circuit.x(1 )

						# Map the quantum measurement to the classical bits
						circuit.measure([0, 1]     ,      [0, 1] )

						# Execute the circuit on the qasm simulator
						lowerCAmelCase   :Dict						=			qiskit.execute(a__     ,      a__     ,      shots=10_00 )

						# Return the histogram data of the results of the experiment.
						return job.result().get_counts(a__ )


if __name__ == "__main__":
		__SCREAMING_SNAKE_CASE   =   single_qubit_measure(2, 2)
		print(F"""Total count for various states are: {counts}""")